@inproceedings{alberti-etal-2019-fusion,
    title = "Fusion of Detected Objects in Text for Visual Question Answering",
    author = "Alberti, Chris  and
      Ling, Jeffrey  and
      Collins, Michael  and
      Reitter, David",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1219",
    doi = "10.18653/v1/D19-1219",
    pages = "2131--2140",
    abstract = "To advance models of multimodal context, we introduce a simple yet powerful neural architecture for data that combines vision and natural language. The {``}Bounding Boxes in Text Transformer{''} (B2T2) also leverages referential information binding words to portions of the image in a single unified architecture. B2T2 is highly effective on the Visual Commonsense Reasoning benchmark, achieving a new state-of-the-art with a 25{\%} relative reduction in error rate compared to published baselines and obtaining the best performance to date on the public leaderboard (as of May 22, 2019). A detailed ablation analysis shows that the early integration of the visual features into the text analysis is key to the effectiveness of the new architecture. A reference implementation of our models is provided.",
}